Linear Ridge Regression Research Articles

Research on Impact Prediction Model for Corn Ears by Integrating Motion Features Using Machine Learning Algorithms

The mechanical damage of corn kernels during harvest leads to mildew in the kernel storage process, seriously affecting food safety and quality. Impact force is the primary source of mechanical damage in the corn threshing process, and its accurate detection is of great significance for corn threshing with low damage. A method for the impact force detection of corn ears was proposed in this manuscript. The momentum theorem determined the main factors influencing impact force (weight, falling height, and space attitude). Corn ear weight, falling height, and space attitude were used as experimental factors. The bench test was carried out with the impact force of corn ear as the output variable. During the experiment, piezoelectric sensors were used to collect the impact force of corn ears under different motion states. Then, the impact force detection model was constructed using four machine learning algorithms: multiple linear regression, ridge regression, random forest, and support vector regression. The results showed that the RF algorithm was more suitable for constructing the prediction model of average and maximum impact force when corn ears fall, SD, RMSE, and r were, respectively: 0.9526, 1.2685, 0.9855; 3.8389, 3.6071, and 0.8510. Secondly, the weight characteristics had the most significant influence on the impact force detection of the ear. Therefore, this method can be used as an accurate, objective, and efficient online detection method for impact force.

Study on prediction model of nitrogen emission in the production stage of residential building materials—A case study of Guangdong province

This study aims to reduce nitrogen emissions from residential buildings and establishes a prediction model for nitrogen emissions during the production of building materials. The calculation boundary, content, and method of nitrogen emission in the production stage of residential building materials are accurately analyzed. Based on the nitrogen emission data of 20 residential buildings in Guangdong, the composition and distribution characteristics of nitrogen emission in the production stage of building materials are analyzed. The coupling relationship between building design parameters and building materials’ nitrogen emissions is established using linear regression and ridge regression. 10 kinds of nitrogen emission prediction models based on the design parameters of residential buildings in the production stage of building materials were established and verified. The results show that the linear model M3, based on the number of floors above and below ground, the area width and depth, and the ridge regression model M5, based on the number of floors above and below ground, the area width and depth, and the total number of main functional rooms, have good fitting and prediction performance, respectively. The linear regression model M6, based on the number of floors above and below ground, the area width and depth, and the total number of rooms, has the best fitting and prediction performance. M3, M5, and M6 can accurately predict the nitrogen emission composition and distribution characteristics of building materials in residential building design and lay a foundation for future research on nitrogen emission evaluation and calculation methods and nitrogen emission reduction technology strategies.

Predictive digital twin driven trust model for cloud service providers with Fuzzy inferred trust score calculation

Cloud computing has become integral to modern computing infrastructure, offering scalability, flexibility, and cost-effectiveness. Trust is a critical aspect of cloud computing, influencing user decisions in selecting Cloud Service Providers (CSPs). This paper provides a comprehensive review of existing trust models in cloud computing, including agreement-based, SLA-based, certificate-based, feedback-based, domain-based, prediction-based, and reputation-based models. Building on this foundation, we propose a novel methodology for creating a trust model in cloud computing using digital twins for CSPs. The digital twin is augmented with a fuzzy inference system, which computes the trust score of a CSP based on trust-related parameters. The architecture of the digital twin with the fuzzy inference system is detailed, outlining how it processes security parameter values obtained through penetration testing mechanisms. These parameter values are transformed into crisp values using a linear ridge regression function and then fed into the fuzzy inference system to calculate a final trust score for the CSP. The paper also presents the outputs of the fuzzy inference system, demonstrating how different security parameter inputs yield various trust scores. This methodology provides a robust framework for assessing CSP trustworthiness and enhancing decision-making processes in cloud service selection.

The Correlation Between the Willingness to Marry and the Household Savings Rate in China

In recent years, the decrease in household savings rate and willingness to marry is an important problem facing society in China. The traditional competitive savings theory holds that the imbalance of the male and female sex ratio is an important factor affecting the household savings rate. However, in the context of the saving rate declining year by year after 2010, it is found that the traditional competitive savings theory can not fully explain the reasons for its decline. At the same time, according to the data statistics, it is found that the trend of marriage intention in China during 2000-2021 is similar to that of the savings rate, which increases first and then decreases. Therefore, this paper established a multiple linear regression model and ridge regression model for regression analysis, and observed the size of the regression coefficient and the positive and negative effects of willingness to marry on household savings rate in China. The regression results show that for every 1% change in willingness to marry, the savings rate will change 13.526% in the same direction on average. This indicates that the decline in willingness to marry plays a positive role in explaining the decline of household savings rate.

Distributed Wearable Ultrasound Sensors Predict Isometric Ground Reaction Force.

Rehabilitation from musculoskeletal injuries focuses on reestablishing and monitoring muscle activation patterns to accurately produce force. The aim of this study is to explore the use of a novel low-powered wearable distributed Simultaneous Musculoskeletal Assessment with Real-Time Ultrasound (SMART-US) device to predict force during an isometric squat task. Participants (N = 5) performed maximum isometric squats under two medical imaging techniques; clinical musculoskeletal motion mode (m-mode) ultrasound on the dominant vastus lateralis and SMART-US sensors placed on the rectus femoris, vastus lateralis, medial hamstring, and vastus medialis. Ultrasound features were extracted, and a linear ridge regression model was used to predict ground reaction force. The performance of ultrasound features to predict measured force was tested using either the Clinical M-mode, SMART-US sensors on the vastus lateralis (SMART-US: VL), rectus femoris (SMART-US: RF), medial hamstring (SMART-US: MH), and vastus medialis (SMART-US: VMO) or utilized all four SMART-US sensors (Distributed SMART-US). Model training showed that the Clinical M-mode and the Distributed SMART-US model were both significantly different from the SMART-US: VL, SMART-US: MH, SMART-US: RF, and SMART-US: VMO models (p < 0.05). Model validation showed that the Distributed SMART-US model had an R2 of 0.80 ± 0.04 and was significantly different from SMART-US: VL but not from the Clinical M-mode model. In conclusion, a novel wearable distributed SMART-US system can predict ground reaction force using machine learning, demonstrating the feasibility of wearable ultrasound imaging for ground reaction force estimation.

Machine Learning to Predict Prostate Artery Embolization Outcomes

PurposeThis study leverages pre-procedural data and machine learning (ML) techniques to predict outcomes at one year following prostate artery embolization (PAE).Materials and MethodsThis retrospective analysis combines data from the UK-ROPE registry and patients that underwent PAE at our institution between 2012 and 2023. Traditional ML approaches, including linear regression, lasso regression, ridge regression, decision trees and random forests, were used with leave-one-out cross-validation to predict international prostate symptom score (IPSS) at baseline and change at 1 year. Predictors included age, prostate volume, Qmax (maximum urinary flow rate), post-void residual volume, Abrams-Griffiths number (urodynamics score) and baseline IPSS (for change at 1 year). We also independently confirmed our findings using a separate dataset. An interactive digital user interface was developed to facilitate real-time outcome prediction.ResultsComplete data were available in 128 patients (66.7 ± 6.9 years). All models predicting IPSS demonstrated reasonable performance, with mean absolute error ranging between 4.9–7.3 for baseline IPSS and 5.2–8.2 for change in IPSS. These numbers represent the differences between the patient-reported and model-predicted IPSS scores. Interestingly, the model error in predicting baseline IPSS (based on objective measures alone) significantly correlated with the change in IPSS at 1-year post-PAE (R2 = 0.2, p < 0.001), forming the basis for our digital user interface.ConclusionThis study uses ML methods to predict IPSS improvement at 1 year, integrated into a user-friendly interface for real-time prediction. This tool could be used to counsel patients prior to treatment.

Block-scale Oil Palm Yield Prediction Using Machine Learning Approaches Based on Landsat and MODIS Satellite Data

Due to environmental threats and weather uncertainty concerns, oil palm yield prediction is crucial for sustaining crop production. This can be achieved through machine learning and utilising remotely sensed data to predict crop yield. However, the comparative studies on remotely sensed data in adopting the machine learning models are still limited due to the data accessibility. Therefore, we compare and evaluate the prediction accuracy between different satellites, namely MODIS and Landsat-7, using machine learning algorithms and the topology of deep neural networks. Random forest and stacking outperformed linear regression, ridge regression, and lasso regression for both Landsat-7 NDVI (R2= 0.78–0.80; RMSE=1.00- 1.26 tonnes per hectare; MAE=0.77- 0.79 tonnes per hectares; MAPE=0.03-0.04 tonnes per hectare) and MODIS NDVI (R2= 0.60–0.65 tonnes per hectares; RMSE= 2.72–2.81 tonne per hectares; MAE= 1.42-1.55, MAPE= 1.01- 1.02 tonnes per hectares). The Landsat-7 NDVI revealed that neural networks with a deeper network topology (R2= 0.85; RMSE= 1.42 tonnes per hectare; MAE=0.57 tonnes per hectares; MAPE=0.06 tonnes per hectare) outperformed neural networks with a baseline and broader network topologies in terms of performance. In contrast, MODIS-NDVI revealed that the neural network with a wider network topology had the highest overall prediction accuracy and the lowest prediction error (R2= 0.75; RMSE= 2.81 tonnes per hectare; MAE=2.27 tonnes per tonnes; MAPE= 0.13). Because of its higher spatial resolution in comparison to MODIS, landsat-7 NDVI used in neural networks with a deep network topology provided the best model performance. Although the use of NDVI as a single input factor may cause uncertainty in some extents, it is an efficient and reliable method for improving yield estimation with the use of medium-resolution satellites, which has important implications for early warning towards the reduction in yield production.

Machine Learning-Based Framework for Failure Forecast and Shear Strength Estimation of Non-Conforming RC Shear Walls

ABSTRACT Earthquake reconnaissance after the recent Kahramanmaras, Turkey, earthquake sequence in February 6, 2023 has shown that the majority of the reinforced concrete (RC) structural elements do not comply with the Turkish Building Earthquake Code (TBEC 2018) requirements. Accurate estimation of the actual strength of existing non-compliant RC structural members is critical to accomplish reliable earthquake performance assessments. This study aims to propose a machine learning-based framework to establish a realistic failure forecast as well as an accurate shear strength estimation depending on the expected failure modes of such walls. To achieve this, shear wall design properties are assigned as inputs whereas failure mode and shear strength were designated as outputs for the corresponding classification and regression problems, respectively. Widely used machine learning methods, namely: Ridge Linear Regression, Logistic Regression, Multi-layer Perceptron, Support Vector Machine, Random Forest, XGBoost, LightGBM, and CatBoost are employed using a database consisting of 432 non-conforming shear walls. The data is randomly split to train and test sets (80% and 20%, respectively) to develop the predictive models and to evaluate model performances, respectively. Performance metrics (e.g. R 2 , RMSE) are evaluated over a hundred random splits to ensure robustness. The proposed CatBoost-based models achieve overall accuracies above 90% for both failure prediction and shear strength estimation. The proposed framework is believed to be a promising tool for earthquake engineers for practical and accurate failure mode forecast and shear strength estimation of non-compliant RC shear walls.

Explainable machine learning: Compressive strength prediction of FRP-confined concrete column

Fibre reinforced polymers (FRP) are widely used for the strengthening of beams, slabs and columns due to their light weight and high strength. Compared to the traditional methods like steel cages or steel jackets, it reduces the time and economic cost significantly. This study focuses on the prediction of the compressive strength of FRP-confined concrete columns, which is influenced by multiple parameters. Initially, through experimental observations and the analysis of parameter mechanisms, the key parameters affecting compressive strength were identified. Six prediction models, namely linear regression, ridge regression, decision tree, random forest, and eXtreme Gradient Boosting, were subsequently constructed and evaluated to determine the optimal machine learning model. A comparison between the machine learning models and mathematical models was conducted. Furthermore, SHapley Additive exPlanation (SHAP) analysis was performed to ascertain the parameter importance and sensitivity of the machine learning model, and the analysis results were compared with existing models. The results indicate that compared to other machine learning and mathematical models, the eXtreme Gradient Boosting model demonstrates superior robustness and accuracy. The thickness and elastic modulus of FRP, as well as the concrete strength, are identified as crucial parameters that positively influence compressive strength. The relationships between the former two parameters and compressive strength follow a logarithmic function, while the relationship between concrete strength and compressive strength is approximately linear. These findings hold implications for the future development of codes and standards.

Predicting Summer Precipitation Anomalies in the Yunnan–Guizhou Plateau Using Spring Sea-Surface Temperature Anomalies

By constructing a correlation network between global sea surface temperature anomalies (SSTAs) and summer precipitation anomalies in the Yunnan–Guizhou Plateau, key SST regions influencing summer precipitation anomalies in the Yunnan–Guizhou Plateau were selected. It was found that spring SSTAs in the Bay of Bengal, southwestern Atlantic, and eastern Pacific are crucial for influencing summer precipitation anomalies in the Yunnan–Guizhou Plateau. Setting SSTAs from these three regions as predictor variables 3 months in advance, we constructed multiple linear regression (MLR), ridge regression (RR), and lasso regression (LR) models to predict summer precipitation anomalies over the Yunnan–Guizhou region. The training phase involved data spanning from 1961 to 2005, which aimed to predict precipitation anomalies in the Yunnan–Guizhou Plateau for the period extending from 2006 to 2022. Based on MLR, RR, and LR models, the correlations between predicted values and observed summer precipitation anomalies in Yunnan–Guizhou were 0.48, 0.46, and 0.46, respectively. These values were all higher than the correlation coefficients of the NCC_CSM model’s predicted and observed values. Additionally, its performance in predicting summer precipitation anomalies over the Yunnan–Guizhou region, based on key SST regions, was assessed using performance metrics such as anomaly correlation coefficient (ACC), anomaly sign consistency rate (PC), and trend anomaly comprehensive score (PS score). The average ACC of MLR, RR, and LR models was higher than that of the NCC_CSM model’s predictions. For MLR, RR, LR, and NCC_CSM models, the PCs exceeding 50% of the year were 14, 14, 11, and 10, respectively. Furthermore, the average PS score for predicting summer precipitation anomalies over the Yunnan–Guizhou region using MLR, RR, and LR was approximately 73 points; 8 higher than the average PS score of the NCC_CSM model. Therefore, predicting summer precipitation anomalies over the Yunnan–Guizhou region based on key SST regions is of great significance for improving the prediction skills of precipitation anomalies in this region.

Enhancing Heartbeat Classification through Cascading Next Generation and Conventional Reservoir Computing

Electrocardiography (ECG) is a simple and safe tool for detecting heart conditions. Despite the diaspora of existing heartbeat classifiers, improvements such as real-time heartbeat identification and patient-independent classification persist. Reservoir computing (RC) based heartbeat classifiers are an emerging computational efficiency solution that is potentially recommended for real-time concerns. However, multiclass patient-independent heartbeat classification using RC-based classifiers has not been considered and constitutes a challenge. This study investigates patient-independent heartbeat classification by leveraging traditional RC and next-generation reservoir computing (NG-RC) solely or in a cascade. Three RCs were investigated for classification tasks: a linear RC featuring linear internal nodes, a nonlinear RC with a nonlinear internal node, and an NG-RC. Each of these has been evaluated independently using either linear ridge regression or multilayer perceptron (MLP) as readout models. Only three classes were considered for classification: the N, V, and S categories. Techniques to deal with the imbalanced nature of the data, such as the synthetic minority oversampling technique (SMOTE) and oversampling by replacement, were used. The MIT-BIH dataset was used to evaluate classification performance. The area under the curve (AUC) criterion was used as an evaluation metric. The NG-RC-based model improves classification performance and mitigates the overfitting issue. It has improved classification performance by 4.18% and 2.31% for the intra-patient and inter-patient paradigms, respectively. By cascading RC and NG-RC, the identification performance of the three heartbeat categories is further enhanced. AUCs of 97.80% and 92.09% were reported for intra- and inter-patient scenarios, respectively. These results suggest promising opportunities to leverage RC technology for multiclass, patient-independent heartbeat recognition.

Cricket Score Predictor using Machine Learning

In cricket, particularly the twenty20 format is most watched and loved by the people, where no one can guess who will win the match until the last ball of the last over. In India, The Indian Premier League (IPL) started in 2008 and now it is the most popular T20 league in the world. So we decided to develop a machine learning model for predicting the outcome of its matches. Winning in a Cricket Match depends on many key factors like a home ground advantage, past performances on that ground, records at the same venue, the overall experience of the players, record with a particular opposition, and the overall current form of the team and also the individual player. This paper briefs about the key factors that affect the result of the cricket match and the regression model that best fits this data and gives the best predictions. In these the score prediction includes linear regression, lasso regression and ridge regression whereas in winning prediction SVC classifier, decision tree classifier and random forest classifier are used. The model used the supervised machine learning algorithm to predict the winning. Random Forest Classifier used for good accuracy and the stable accuracy so that desired predicted output is accurate

Estimating Body Mass Index from Facial Images

Abstract: A person's health status may have a significant impact on many aspects of their life, from mental health to lifespan to financial security. The health of a person can be calculated by a value which is called Body Mass Index (BMI), it uses both the height and weight of a person. Numerous variables, including physical health, mental health, and popularity, have been linked to BMI. With the increasing number of people being obese, self-diagnostic solutions for healthy weight monitoring are grabbing significant attention. Calculating BMI using the statistical formula requires precise measurements of the height and weight of a person and is time-consuming. The main objective of this project is to predict the BMI of a person by giving the image as input. While developing Fitness apps, we can use this system to detect the BMI of a person daily and suggest suitable exercises. The developed system can also be used to find whether a person is suffering from malnutrition and some other diseases that can be detected using BMI. The models used in our project are FaceNet, Ridge Linear Regression, Random Forest Regression, Support Vector Regression, and ensemble of regression models.

Forecasting Energy Utilization in Residential Areas through Machine Learning Techniques

Analyzing and forecasting energy use in residential areas and buildings is critical for promoting sustainability and optimising energy usage. This paper describes a unique way for exact energy consumption analysis and forecasting that employs machine learning techniques such as linear regression and ridge regression. The proposed model intends to provide important insights and forecasts by combining historical energy data with relevant parameters such as building attributes, weather conditions, and tenant behaviour, improving energy usage in residential settings. The expected prototype is tested using a dataset accessible on Kaggle. The findings provided in the study show that our suggested model surpasses the state-of-the-art system, with accuracy rates of 66.56 percent for linear regression and 70.12 percent for ridge regression, respectively.

EXPERIMENTAL RELATIONSHIP BETWEEN LAND USE/LAND COVER AND UNDERGROUND FLOW IN AL BA COMMUNE IN THE CENTRAL REGION OF VIETNAM

Rapid urbanization and intensive agricultural practices in Vietnam's Central Highlands have substantially transformed land use/land cover (LULC) patterns, with implications for groundwater resources necessitating measurement. The objective of this investigation is to examine the links between multi-temporal variations in LULC and changes in underground water flow in Al Ba Commune, Gia Lai Province. Supervised object-based classification of Landsat and Sentinel imagery from the past 25 years has produced consistent LULC maps as inputs for the model. Geospatial data of LULC, meteorological data, and in-situ data of groundwater flow (Q) were used to calibrate and validate the integrated hydrological MIKE SHE model and simulate past Q conditions for correlation analysis. With these data on LULC and groundwater flow, this study investigates the relationship between LULC changes and Q using Multivariate Linear Regression (MLR) and Ridge Regression models. The MLR model showed high R² (0.91) but positive impacts of all LULC classes on Q, contrary to expectations. Further analysis with Variance Inflation Factor (VIF) revealed multicollinearity among LULC variables, which was addressed using Ridge Regression with a bias factor to correct for this issue. The revised model demonstrated that non-vegetative covers negatively affect Q while vegetative covers have a positive impact. These results highlight the complex dynamics of LULC changes on groundwater resources, which can be detected by statistical approaches. The results of study could be important for sustainable land management.

Fundamental and Practical Feasibility of Electrocardiogram Reconstruction from Photoplethysmogram.

Electrocardiogram (ECG) reconstruction from contact photoplethysmogram (PPG) would be transformative for cardiac monitoring. We investigated the fundamental and practical feasibility of such reconstruction by first replicating pioneering work in the field, with the aim of assessing the methods and evaluation metrics used. We then expanded existing research by investigating different cycle segmentation methods and different evaluation scenarios to robustly verify both fundamental feasibility, as well as practical potential. We found that reconstruction using the discrete cosine transform (DCT) and a linear ridge regression model shows good results when PPG and ECG cycles are semantically aligned-the ECG R peak and PPG systolic peak are aligned-before training the model. Such reconstruction can be useful from a morphological perspective, but loses important physiological information (precise R peak location) due to cycle alignment. We also found better performance when personalization was used in training, while a general model in a leave-one-subject-out evaluation performed poorly, showing that a general mapping between PPG and ECG is difficult to derive. While such reconstruction is valuable, as the ECG contains more fine-grained information about the cardiac activity as well as offers a different modality (electrical signal) compared to the PPG (optical signal), our findings show that the usefulness of such reconstruction depends on the application, with a trade-off between morphological quality of QRS complexes and precise temporal placement of the R peak. Finally, we highlight future directions that may resolve existing problems and allow for reliable and robust cross-modal physiological monitoring using just PPG.

Modelling Stock Prices of Energy Sector using Supervised Machine Learning Techniques

This paper aims at comparing the performance of the different state-of-the-art machine learning techniques in anticipating the performance of stock prices of the energy sector. The data collected cover the period from January 2020 to February 2023 with a daily frequency for the three most imported refined petroleum products in Morocco and trained four regression machines learning (linear regression, lasso regression, ridge regression, and SVR) and four classifiers machine learning (logistic regression, decision tree, extra tree and Random Forest) so that anticipating one day ahead prices direction can take place no matter whether they are negative or positive prices. The performance of regression algorithm is then evaluated using different evaluation metrics, especially MSE, RMSE, MAE, MAPE and R2 to evaluate the performance of regression algorithm while precision, recall and F1 scores are used to evaluate the performance of classifiers algorithm. The outcomes propose that the performance of linear regression and ridge regression takes place equally and outperform other single regression that is lasso regression and SVR for-one-day predictions as a whole. In addition to that, we have come to find that in the classifiers, algorithms group all machine learning display similar predictive accuracy, this is on one hand. On the other hand, the best of them is the logistic regression. In brief, this study suggests that all performance metrics are significantly improved by ensemble learning. Therefore, this study proves that critical information affecting stock movement can be captured by utilizing historical transactions.

Predicting immunogenicity of COVID-19 vaccines in hemodialysis patients with renal disease

Individuals who are diagnosed with chronic kidney disease, particularly those receiving maintenance hemodialysis treatment, face a greater likelihood of suffering from severe symptoms and fatality due to COVID-19. This study aimed to explore the optimal vaccination approach for these individuals. The study used data analysis tasks such as data preprocessing, cleaning, and exploration, and machine learning models including linear regression, random forest, XGBoost, gradient boosting, AdaBoost, decision trees, Lasso, and ridge regression were used to construct the predictive model. The study found that the Lasso model performed the best overall in predicting anti-S IgG antibodies levels in response to COVID-19 vaccines for people with kidney failure with MAE of 8.81, RMSE of 19.59, and R2 value of 0.93. The adjusted R2 value for the Lasso model was also 0.93, indicating that the model's ability to explain the variance in the data was not affected by the number of predictors in the model. The Random Forest model best predicted the duration of immunogenicity, with R2 and adjusted R2 values of 0.71 and 0.69, respectively. The ensemble model that includes all eight models, i.e., Ridge, Lasso, Linear Regression, Random Forest, AdaBoost, Gradient Boosting, XGBoost, and Decision Tree, has the best performance with the lowest MAE, the lowest RMSE, the highest R2, and the highest adjusted R2 values of 3.91, 5.00, 0.73, and 0.72, respectively. However, further research is required to validate these models and extend their application to different populations and vaccine types, as well as considering other factors that may affect immune response to COVID-19 vaccines. These findings can be helpful in improving vaccination strategies and promoting public health.

Comparative analysis of different approaches to the Ukrainian residential property market evolution modelling and its forecast for the years 2019–2024

PurposeThis study aims to provide a comprehensive analysis of various approaches to the residential property market evolution modelling and to examine the macroeconomic fundamentals that have shaped this market development in Ukraine in recent years.Design/methodology/approachThe study uses a comprehensive data set encompassing relevant macroeconomic indicators and historical apartment prices. Multifactor linear regression (MLR) and ridge regression (RR) models are constructed to identify the impact of multiple predictors on apartment prices. Additionally, the ARIMAX model integrates time series analysis and external factors to enhance modelling and forecasting accuracy.FindingsThe investigation reveals that MLR and RR yield accurate predictions by considering a range of influential variables. The hybrid ARIMAX model further enhances predictive performance by fusing external indicators with time series analysis. These findings underscore the effectiveness of a multidimensional approach in capturing the complexity of housing price dynamics.Originality/valueThis research contributes to the real estate modelling and forecasting literature by providing an analysis of multiple linear regression, RR and ARIMAX models within the specific context of property price prediction in the turbulent Ukrainian real estate market. This comprehensive analysis not only offers insights into the performance of these methodologies but also explores their adaptability and robustness in a market characterized by evolving dynamics, including the significant influence of external geopolitical factors.

Application of the performance of machine learning techniques as support in the prediction of school dropout

This article presents a study, intending to design a model with 90% reliability, which helps in the prediction of school dropouts in higher and secondary education institutions, implementing machine learning techniques. The collection of information was carried out with open data from the 2015 Intercensal Survey and the 2010 and 2020 Population and Housing censuses carried out by the National Institute of Statistics and Geography, which contain information about the inhabitants and homes. in the 32 federal entities of Mexico. The data were homologated and twenty variables were selected, based on the correlation. After cleaning the data, there was a sample of 1,080,782 records in total. Supervised learning was used to create the model, automating data processing with training and testing, applying the following techniques, Artificial Neural Networks, Support Vector Machines, Linear Ridge and Lasso Regression, Bayesian Optimization, Random Forest, the first two with a reliability greater than 99% and the last with 91%.